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See for details Area detector mask examples.
Instantiation of the Mask object
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from psana.detector.mask import Mask, DTYPE_MASK kwa = np.uint8 {'status':True, 'status_bits':0xffff, 'gain_range_inds':(0,1,2,3,4),\ 'neighbors':False, 'rad':3, 'ptrn':'r',\ 'edges':False, 'width':0, 'edge_rows':10, 'edge_cols':5,\ 'center':False, 'wcenter':0, 'center_rows':5, 'center_cols':3,\ 'calib':False,\ 'umask':None,\ 'force_update':False, 'dtype':DTYPE_MASK} m = Mask(det, **kwa) m = Mask(det) # minimal version. |
Combined and separate mask methods
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m.set_mask(**kwa) # forced update of cached mask. mask = om._mask_default(dtype=DTYPE_MASK)(**kwa) # returns cached mask. mask = om._mask_calibdefault() mask = om._mask_calib_or_default(dtype=DTYPE_MASK)) # if available returns mask from "pixel_mask" calibration type, otherwise array of ones. mask = om._mask_from_status(status_bits=0xffff, gain_range_inds=(0,1,2,3,4), dtype=DTYPE_MASK, **kwa) mask = o._mask_neighbors(mask, rad=9, ptrn='r') mask = o._m.mask_edges(width=0, edge_rows=1, edge_cols=1, dtype=DTYPE_MASK, **kwa) mask = om._mask_center(wcenter=0, center_rows=1, center_cols=1, dtype=DTYPE_MASK, **kwa) mask = om._mask(status=True, status_bits=0xffff, gain_range_inds=(0,1,2,3,4)\ neighbors=False_neighbors(mask, rad=39, ptrn='r',\ edges) |
- default combined mask parameters (status=True,
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- neighbors=
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- False, edges=False, center=False, calib=False, umask=None) are set to define mask for pixel_status only.
- mask is used in det.raw.calib/image mask at evaluation of common mode correction, but by default it is not applied to data.
- to apply mask to data use multiplication calib *= mask.
Other useful area detector methods
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